Receiverdriven Layered Multicast

1
Receiver-driven Layered Multicast

• Paper by- Steven McCanne, Van Jacobson and Martin
  Vetterli ACM SIGCOMM 1996
• Presented By Manoj Sivakumar

2
Overview

• Introduction
• Approaches to Rate-Adaptive Multimedia
• Issues and challenges
• RLM - Details
• Performance Evaluation
• Conclusions

3
Introduction

• Consider a typical streaming Application
• What rate should the source send data at ?

Receiver
Source
Internet
128 Kb/s
X Kb/s
4
Approaches to Rate-Adaptive Multimedia

• Rate Adaptation at Source based on available
  network capacity
• Works well for a Unicast environment
• How about multicast ?

Receiver 1
X1 Kb/s
source
Receiver 2
128 Kb/s
X2 Kb/s
Receiver 3
X3 Kb/s
5
Example of Heterogeneity
6
Issues and Challenges

• Optimal link utilization
• Best possible service to all receivers
• Ability to cope with Congestion in the network
• All this should be done with just best effort
  service on the internet

7
Layered Approach

• Rather than sending a single encoded video signal
  the source sends several layers of encoded signal
  each layer incrementally refining the quality
  of the signal
• Intermediate Routers drop higher layers when
  congestion occurs

8
Layered Approach

• Each layer is sent to one multicast group
• If a receiver wants higher quality subscribes
  to all higher level layer multicast groups

9
Issue in Layered Approach

• No framework for explicit signaling between the
  receivers and routers
• A mechanism to adapt to both static heterogeneity
  and dynamic variations in network capacity is not
  present
• Solution - RLM

10
RLM Network Model

• Works with IP Multicast
• Assume
• Best effort (packets may be out of order, lost or
  arbitrarily delayed)
• Multicast (traffic flows only along links with
  downstream recipients)
• Group oriented communication (senders do not know
  of receivers and receivers can come and go)
• Receivers may specify different senders

11
RLM - Video Streams

• One channel per layer
• Layers are additive
• Adding more channels gives better quality
• Adding more channels requires more bandwidth

12
RLM Sessions

• Each session composed of layers, with one layer
  per group
• Layers can be separate (i.e. each layer is higher
  quality) or additive (add all to get maximum
  quality)
• Additive is more efficient

13
Router Mechanisms

• Dropping of packets
• Drop less preferential packets first

14
RLM - Protocol

• Abstraction
• on congestion, drop a layer
• on spare capacity, add a layer

15
RLM Adding and Dropping layers

• Drop layer when packet loss
• Add does not have counter-part signal
• Need to try adding at well-chosen times
• Called join experiment

16
RLM Adding and Dropping layers

• If join experiment fails
• Drop layer, since causing congestion
• If join experiment succeeds
• One step closer to operating level
• But join experiments can cause congestion
• Only want to try when might succeed

17
RLM Join Experiments

• Get lowest layer and start timer for next probe
• Initially timer small
• If higher level fails then increase timer
  duration else proceed to next layer and start
  time for the layer above it
• Repeat until optimum

18
RLM Join Experiment

• How to know is join experiment succeeded
• Detection time

19
Detection Time

• Hard to estimate
• Can only be done experimentally
• Initially start with a large value
• Progressively update the detection time based on
  actual values

20
RLM - Issues with Joins

• Is this Scalable
• What if each node does join experiments and the
  same time for different layers
• Wrong info to node that requests lower layer if
  the other node had requested higher layer
• Solution Shared Learning

21
RLM Shared Learning

• Each node broadcasts its intent to the group
• Advs other nodes can learn from the result of
  this nodes experiment
• Reduction in simultaneous experiments
• Is this still foolproof ??

22
RLM - Evaluation

• Simulations performed in NS
• Video modeled as CBR
• Parameters
• Bandwidth 1.5 Mbps
• Layers 6, each 32 x 2m kbps (m 0 5)
• Queue management Drop Tail
• Queue Size (20 packets)
• Packet size (1 Kbytes)
• Latency (varies)
• Topology (next slide)

23
RLM - Evaluation

• Topologies
• 1 explore latency
• 2 explore scalability
• 3 heterogeneous with two sets
• 4 large number of independent sessions

24
RLM Performance Metrics

• Worse-case lost rate over varying time intervals
• Short-term how bad transient congestion is
• Long-term how often congestion occurs
• Throughput as percent of available
• But will always be 100 eventually
• So, look at time to reach optimal
• Note, neither alone is ok
• Could have low loss, low throughput
• High loss, high throughput
• Need to look at both

25
RLM Performance Results

• Latency Results

26
RLM Performance Results

• Latency Results

27
RLM Performance Results

• Session Size

28
RLM Performance Results

• Convergence rate

29
RLM Performance Results

• Bandwidth Heterogeneity

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Conclusions

• Possible Pitfalls
• Shared Learning assumes only multicast traffic
• Is this valid ??
• Is congestion produced by Multicast traffic alone
• Simulation does not other traffic requests!!

31
Conclusions

• Overall a nice architecture and mechanism to
  regulate traffic and have the best utilization
• But still needs refinement

32
References

• S. McCanne, V. Jacobson, and M. Vetterli,
  "Receiver-driven layered multicast," in Proc.
  SIGCOMM'96, ACM, Stanford, CA, Aug. 1996, pp.
  117--130.